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Electrocardiosignal classifying method and device, electronic equipment and storage medium

A technology of ECG signal and classification method, which is applied in medical science, sensors, diagnostic recording/measurement, etc., and can solve problems such as wrong recognition results, wrong classification of atrial fibrillation, and difficult detection of waveform features

Active Publication Date: 2019-05-21
GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the P wave or f wave in the ECG signal is a weak signal, and its waveform characteristics are difficult to detect
Moreover, many non-AF types of abnormal rhythms (such as tachycardia, bradycardia, arrhythmia, etc.) show characteristics similar to AF, and if based on conventional ECG signal classification methods, erroneous identification results may occur
[0005] Therefore, the ECG signal classification method in the prior art has the problem of wrong classification of atrial fibrillation

Method used

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  • Electrocardiosignal classifying method and device, electronic equipment and storage medium
  • Electrocardiosignal classifying method and device, electronic equipment and storage medium
  • Electrocardiosignal classifying method and device, electronic equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0144] figure 1 It is a flowchart of an ECG signal classification method provided in Embodiment 1 of the present application. The ECG signal classification method may specifically include the following steps:

[0145] Step S110, extract the signal waveform from the ECG signal.

[0146] In specific implementation, multi-channel synchronization data can be used to collect human heart signals, background noise, and ECG signals. More specifically, first, the ECG signals can be collected through the ECG leads and sensors, and the collected ECG signals can be processed by impedance matching, filtering, and amplification through an analog circuit. Then, the analog-to-digital converter converts the analog signal of the physiological parameters of the human body into a digital signal. Then, the filtered ECG signal is obtained through low-pass filtering technology. Finally, the wavelet transform technique is used to extract the signal waveform from the filtered ECG signal.

[0147] figure ...

Embodiment 2

[0172] Figure 4 It is a flowchart of an ECG signal classification method provided in the second embodiment of the present application. Specifically, refer to Figure 4 , The ECG signal classification method of the second embodiment of this application specifically includes:

[0173] In step S210, the original ECG signal is collected, and the original ECG signal is low-pass filtered to obtain a high-frequency noise filtering signal as the ECG signal.

[0174] In specific implementation, a low-pass digital filter can be used to perform low-pass filtering to filter out high-frequency noise (such as above 300 Hz) to obtain a filtered ECG signal. Among them, the low-pass digital filter may specifically be a Butterworth filter.

[0175] Step S220, extract the signal waveform from the ECG signal.

[0176] In one embodiment, the step S220 includes: extracting the P wave, QRS wave, and T wave from the ECG signal by wavelet transform technology to obtain the signal waveform.

[0177] In the sp...

Embodiment 3

[0324] Figure 7 It is a schematic structural diagram of an ECG signal classification device provided in Embodiment 3 of the present application. reference Figure 7 The ECG signal classification device provided in this embodiment specifically includes: a waveform extraction module 310, a morphological feature acquisition module 320, a statistical feature acquisition module 330, and a classification module 340; among them:

[0325] The waveform extraction module 310 is used to extract the signal waveform from the ECG signal;

[0326] The morphological feature acquiring module 320 is configured to acquire the morphological features of the signal waveform; the morphological features include any one of width features, correction features, slope features, and waveform depth features;

[0327] The statistical feature obtaining module 330 is configured to obtain the morphological statistical feature of the morphological feature, and input the morphological statistical feature to the classi...

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Abstract

The invention relates to an electrocardiosignal classifying method and device, electronic equipment and a storage medium. The electrocardiosignal classifying method includes the steps that a signal waveform is extracted from electrocardiosignals; the morphological characteristic of the signal waveform is obtained, wherein the morphological characteristic comprises any one of the width characteristic, the correction characteristic, the slope characteristic and the waveform depth characteristic; the morphological statistical characteristics of the morphological characteristic are obtained, and are input into a classifier; classification results input by the classifier are obtained, and the signal type of the electrocardiosignals is obtained. According to the technical scheme, abnormal rhythms of various types can be more accurately identified, it is avoided that abnormal rhythms of non-atrial-fibrillation types of tachycardia, bradycardia, arrhythmia and the like are mistakenly classified into abnormal rhythms of atrial-fibrillation types, and the accuracy of electrocardiosignal classifying is increased.

Description

Technical field [0001] This application relates to the field of medical equipment and medical products, and in particular to an ECG signal classification method, device, electronic equipment and storage medium. Background technique [0002] Atrial Fibrillation (AF) is abbreviated as atrial fibrillation. It is the most common arrhythmia disease in clinical practice. It is characterized by disordered atrial activity and subsequent complications such as stroke and myocardial infarction, leading to higher Disability and mortality rate seriously endanger human health and life. The algorithm to study whether there is atrial fibrillation in the ECG signal can be detected and treated early, so that the best opportunity for treatment can be grasped more, and the incidence and mortality of atrial fibrillation can be reduced. Therefore, it has important clinical and social significance. [0003] Since the two important clinical manifestations during the onset of atrial fibrillation are the a...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/04A61B5/046A61B5/361
Inventor 胡静赵巍
Owner GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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